The case for logit margins has already been made in prior work [1]}, [2]}, [3]}, [4]}, [5]}, but is worth succinctly recapitulating.
There are two key arguments:
first, for generic supervised learning problems,
margin bounds [6]}, [7]}, [8]} establish that large margins imply good generalisation.
Second,
for long-tail problems in particular,
excluding logit margins would implicitly seek to model \(\mathbb {P}( y \mid x )\) ;
absent further correction,
this solution will be suboptimal for the balanced error [9]}, [10]}, [3]}, [4]}.
